SAMPLE: Nonprofit & public sector × BI Consulting Services — Program outcomes & funding board (sample)

Nonprofit & public sector × BI Consulting Services

Prepared for Executive directors and program leaders

September 2026

Program outcomes · Funding & fund balances · Donors & volunteers

A board-ready view of what came in, where it went, and what it changed — for a mid-size, four-program, four-site organisation. Every number below responds to the filters; the donor-retention topline is anchored to a published sector benchmark¹.

People served vs target, by month

FY26 (Jul 2025 – Jun 2026) · dotted = FY25

Served (in period)Served (outside period)TargetFY25 same month

What this board answers

Board questions

Dollar in, outcome out — funding-to-impact ribbon

Width = dollars · reads left to right: source → fund type → program → outcome

Outcome attainment vs target, program × site

% of served who reached the program's defined outcome

At / above targetWithin 5 ptsMore than 5 pts below

Spend vs budget & fund balances

By fund · restricted vs unrestricted

SpendBudgetRestricted balanceUnrestricted balance

Grant & funding pipeline

By stage · decision in period

Donor retention vs sector benchmark

Rolling monthly · benchmark 43.3%¹

Volunteer hours by site

Hours logged · vs FY25 same period

Ask the data

AI layer on top of the model · illustrative

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Generated answer

¹ Benchmark: the Fundraising Effectiveness Project (AFP / GivingTuesday Data Commons) Q4 2025 report, published 21 April 2026, put overall donor retention for calendar 2025 at 43.3% (up from 43.1% in 2024). Source: afpglobal.org, "Fundraising Effectiveness Project Reports Strongest Revenue Growth in Five Years, Even as Fewer Donors Give." All other figures on this page are illustrative and internally consistent; they do not describe any real organisation.

Private AI on your own server

The "ask the data" panel above is built to run on a GPU server inside your own network: an open-weight model over your own documents, with every answer citing the file it came from. No per-seat licence, works offline, and nothing leaves the building.

How it sits in your environment

One appliance-class server on your rack or in a secured closet. Your files are indexed where they already live; the model runs locally; answers come back with the source page attached.

What that means in practice

1
Your data only, on your hardwareAn open model over your own documents and exports. No third-party AI processor, no data-transfer agreement to review.
2
Every answer cites its sourceReplies point to the file and page they were drawn from, so people check the answer instead of trusting it blind.
3
No per-seat licenceThe whole team can ask questions. Cost is the server and our build, not a monthly number that grows with headcount.
4
Works offlineIf the internet connection goes down, the answers still come. Nothing depends on a vendor's uptime or pricing changes.